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Dense (or fully connected) hidden layers are layers of neurons that connect to each node in the previous layer by a parameterized synapse. They perform a linear transformation on their input and are usually followed by an Activation layer. The majority of the trainable parameters in a standard feed forward neural network are contained within Dense hidden layers.


# Name Default Type Description
1 neurons int The number of nodes in the layer.
2 l2Penalty 0.0 float The amount of L2 regularization applied to the weights.
3 bias true bool Should the layer include a bias parameter?
4 weightInitializer He Initializer The initializer of the weight parameter.
5 biasInitializer Constant Initializer The initializer of the bias parameter.


use Rubix\ML\NeuralNet\Layers\Dense;
use Rubix\ML\NeuralNet\Initializers\He;
use Rubix\ML\NeuralNet\Initializers\Constant;

$layer = new Dense(100, 1e-4, true, new He(), new Constant(0.0));